The present invention relates to a method for predicting a demand for repair parts in future.
With respect to a product, the number of existing products in future can be predicted roughly from statistics in the past, and a market scale in the future of repair parts of the product can be obtained roughly by multiplying the number of existing products by a presumed exchange rate of the repair parts. The exchange rate is that for repair and approximately equal to a trouble rate.
However, with respect to a functional part, the trouble rate thereof cannot be obtained theoretically. Therefore, the parts havs been manufactured based on a prediction of market scale that was obtained by experiences or perceptions of experts.
Therefore, accuracy of the predicted trouble rate is very low and the degree of confidence of the predicted demand is poor, so that the number of the repair parts manufactured based on such prediction is not suited to actualities. As the result, stock of the repair parts becomes excessive or short.
The present invention has been made in view of the foregoing, and an object of the invention is to provide a method for predicting a demand for repair parts in which trouble rate in the future of the repair parts can be presumed accurately and a reliable demand prediction is possible.
In order to achieve the above object, the present invention provides a method for predicting a demand of repair parts comprising: collecting data of the number of troubles, causes of troubles and the number of years elapsed with respect to repair parts on the basis of the repair parts changed owing to troubles within a guarantee period; extracting troubles owing to durability deterioration from the causes of troubles to sum up the number of troubles owing to durability deterioration for each year elapsed; calculating a trouble rate of the repair parts for each year elapsed from the number of troubles summed up for each year elapsed and the number of existing products for each year elapsed; presuming a trouble rate of the repair parts after the guarantee period has elapsed from the calculated trouble rate of the repair parts for each year elapsed; and calculating market scale in the future of the repair parts from the presumed trouble rate of the repair parts after the guarantee period has elapsed and a presumed number of the existing products in the future to predict a demand for the repair parts.
Since about 100% of the parts getting out of order within the guarantee period are exchanged free of charge, a substantially accurate trouble rate can be obtained based on data regarding the repair parts exchanged owing to troubles within the guarantee period.
Among causes of troubles, causes owing to initial quality poorness are those peculiar to an initial period and will not cause trouble after the guarantee period has elapsed. Therefore, if troubles owing to durability deterioration except for the initial quality poorness are extracted as basic data, an accurate trouble rate can be calculated.
If such an accurate trouble rate within the guarantee period is calculated for each year elapsed, a trouble rate after the guarantee period elapsed can be presumed accurately, and a market scale in the future of the repair parts can be calculated from the trouble rate and the presumed number of the products existing in the future to predict a demand for the repair parts accurately.
In the above-mentioned method for predicting a demand for repair parts, the trouble rate of the repair parts after the guarantee period has elapsed may be presumed by making a Weibull-analysis of the trouble rate of the repair parts for each year elapsed.
Since distribution of life times (trouble times) of articles goes roughly along the Weibull distribution, the trouble rate after the guarantee period has elapsed can be presumed accurately by making the Weibull-analysis of the trouble rate of the repair parts for each year elapsed.